Narrative clinical reports contain a rich set of clinical knowledge that could be invaluable for clinical research. However, they may also contain personally identifiable information (PII) that make those clinical reports classified as PHI, which is associated with use restrictions and risks to privacy. Computational de-identification seeks to remove all instances of PII in such narrative text in order to produce de-identified documents, which would no longer be classified as PHI and can be used in clinical research with fewer constraints and with almost no risk to privacy. Computational de-identification uses artificial intelligence methods including pattern recognition and computational linguistic techniques to recognize words and other alphanumeric tokens denoting PII (e.g., names, addresses, and telephone and social security numbers) in the text, and replace them with labels such as NAME and ADDRESS. In this way, both patient privacy is protected and clinical knowledge is preserved. After exploring existing de-identification tools, the U.S. National Library of Medicine (NLM) began developing a new software application called NLM Scrubber, which is capable of de-identifying many types of clinical reports with high accuracy. The software design is based on both deterministic and probabilistic artificial intelligence methods utilizing large dictionaries of personal names, addresses, and organizations. The application accepts narrative reports in plain text or in HL7 format. When the input reports are formatted as HL7 messages, the application software leverages patient information embedded in HL7 segments to find such information in the text portion of the HL7 message. NLM Scrubber has been downloaded by a number of organizations for testing and use, including IBM, Google, Fred Hutch Cancer Research Center, Harvard Medical School, Florida International University, University of Bristol, University College Dublin, and Oak Ridge National Laboratory. National Cancer Institute (NCI) along with the state cancer registries working with NCI have also been voiced their interests in using NLM Scrubber to de-identify narrative pathology reports in Surveillance, Epidemiology and End Results (SEER) database. Our current focus is making NLM-Scrubber answer various needs of clinical scientists and clinical data managers without a deep understanding of the underlying technology. In this term, we improved run-time performance of the system and provided a graphical user interface for easier use. Our users can now provide white and black lists of terms to better preserve clinical information and to better preserve patient privacy, respectively. In the new version of NLM-Scrubber, the users can also preserve certain allowable PII, producing the so-called Limited Data Sets. While NLM-Scrubber can be used for de-identifying all clinical reports repository-wide, it can also be used in various modes tailored to the user and their context, including on-demand cohort-specific de-identification and de-identification with patient and provider identifiers. NLM-Scrubber enables clinical scientists, the users of de-identified data, to be part of the process and shape the de-identification output based on their needs.